• As an MLE on this team, you will primarily be focused on the models, metrics, pipelines, systems, and services that power and deliver excellence via Yobi Applications products. • This role involves a large degree of 0-to-1 development, and will rely on collaboration with Product, core signals MLEs, and leaning on your own expertise and insight in building holistic ML-powered products. • Significant "wearing your Product hat" is expected, along with driving results in the many domains required to deliver whole ML-powered products - we are a quickly growing startup after all!

• As an MLE on the Core Signals team, you will primarily be focused on (1) the representation learning surrounding our fundamental user-behavioral modeling problems and (2) using those core models to power new and existing products. • This role involves a lot of collaboration with the Product org and Applications team to realize these R&D gains - but is ultimately a "full stack" ML role. Day to day responsibilities include data processing, model training, deployment, and evaluation. • A good amount of "wearing your Product hat" is expected, as well as the ability to flex into some other functions as needed - we are a quickly growing startup after all.

• Design, develop, implement, and fine-tune AI and machine learning models to support web-based applications in secure environments with evolving use cases. • Build and maintain data pipelines, training workflows, and experimentation environments to enable rapid model iteration and evaluation. • Evaluate model performance using quantitative and qualitative metrics (e.g., accuracy, robustness, stability, efficiency, generalization) and translate results into actionable improvements. • Analyze data, model outputs, and experimental results to recommend changes to algorithms, features, data sources, or system architecture. • Proactively identify and assess tools, frameworks, and technologies that best support platform goals, balancing performance, scalability, and maintainability. • Collaborate closely with software developers, data engineers, DevSecOps teams, and stakeholders to integrate AI capabilities into production systems. • Ensure AI and data science solutions are transparent, testable, and maintainable to support long-term operational use. • Communicate technical approaches, assumptions, tradeoffs, and results clearly to both technical and non-technical audiences, including during design reviews and demonstrations.

• Lead and nurture a growing team of machine learning engineers, supporting their career development through coaching and mentorship. • Leading team OKR discussions, coordinating projects and facilitating team meetings, planning and retros. • Collaborate with the CTO, Platform and Product Managers to align team priorities with company OKRs. • Collaborate with the People team on recruiting and onboarding talent that matches our values and technical excellence. • Act as a sounding board for the team, empowering the team, and support identifying and resolving bottlenecks and efficiency blockers, enabling the team to iterate faster. • Drive the development and deployment of ML systems, optimising tools and infrastructure for efficiency, while ensuring timeline and goals are met. • Promoting a culture of collaboration and continuous learning, and mentoring team members in their development.